SEER: Long-Context Reasoning via Selective Visual-Text Compression
Long-context reasoning remains computationally expensive for large language models due to the quadratic complexity of attention over text tokens. Visual-text compression offers a promising alternative by rendering text into images and processing them with vision-language models, often reducing token usage. However, existing approaches apply uniform compression regardless of query relevance, potentially sacrificing precision where detailed extraction is required. We present SEER, a framework that learns to select query-relevant images through visual scanning and retrieve textual content only where needed, combining the efficiency of visual compression with the precision of text-based reaso...
| Authors | Jiawei Xu, Zhilin Zhai, Jinrui Fang et al. |
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| Primary category | cs.CL |
| Published | 16 August 2026 |
| Last updated at source | 16 August 2026 |
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